—-Linear Mixed Effects Model —-

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = county.Demo_and_Covid.500counties)

## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling

print(summary(this.lme), correlation=TRUE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -1193.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9464 -0.3479 -0.0901  0.1832  5.6340 
## 
## Random effects:
##  Groups   Name        Variance   Std.Dev.
##  stateID  (Intercept) 0.00000127 0.001127
##  Residual             0.00001400 0.003741
## Number of obs: 178, groups:  stateID, 33
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0101625431   0.0096461051  67.4091819386
## Affluence                    0.0047355148   0.0011186418  99.1444625092
## Singletons.in.Tract          0.0014639303   0.0009213236 139.3445969200
## Seniors.in.Tract             0.0008853489   0.0012102208 148.6380024753
## African.Americans.in.Tract   0.0005927335   0.0010163699 150.8947551957
## Noncitizens.in.Tract         0.0009039769   0.0007848329 126.6001382084
## High.BP                      0.0001920554   0.0001913059 108.3638445796
## Binge.Drinking               0.0001465824   0.0001585600  40.7864655196
## Cancer                      -0.0009292047   0.0011120054  97.4697709685
## Asthma                       0.0006765186   0.0005596267  39.7588383820
## Heart.Disease                0.0011067513   0.0013182362  71.7051231099
## COPD                        -0.0001480712   0.0010913908  74.2123187620
## Smoking                     -0.0000934126   0.0002291567  77.8719033880
## Diabetes                    -0.0005659314   0.0005403132  79.1778221150
## No.Physical.Activity        -0.0000136879   0.0002069196  87.1565579792
## Obesity                      0.0002401006   0.0001788037  95.2115962596
## Poor.Sleeping.Habits        -0.0000100415   0.0001670882 122.1881581737
## Poor.Mental.Health          -0.0000738256   0.0004188868  30.4092909367
## Testing_Rate                 0.0000005249   0.0000002843  34.1444000861
## Hospitalization_Rate        -0.0000946979   0.0000899134  26.9871466499
##                            t value  Pr(>|t|)    
## (Intercept)                 -1.054    0.2959    
## Affluence                    4.233 0.0000515 ***
## Singletons.in.Tract          1.589    0.1143    
## Seniors.in.Tract             0.732    0.4656    
## African.Americans.in.Tract   0.583    0.5606    
## Noncitizens.in.Tract         1.152    0.2516    
## High.BP                      1.004    0.3177    
## Binge.Drinking               0.924    0.3607    
## Cancer                      -0.836    0.4054    
## Asthma                       1.209    0.2339    
## Heart.Disease                0.840    0.4039    
## COPD                        -0.136    0.8924    
## Smoking                     -0.408    0.6847    
## Diabetes                    -1.047    0.2981    
## No.Physical.Activity        -0.066    0.9474    
## Obesity                      1.343    0.1825    
## Poor.Sleeping.Habits        -0.060    0.9522    
## Poor.Mental.Health          -0.176    0.8613    
## Testing_Rate                 1.846    0.0736 .  
## Hospitalization_Rate        -1.053    0.3016    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Correlation of fixed effects could have been required in summary()

## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.135                                                        
## Sngltns.n.T  0.019  0.077                                                 
## Snrs.n.Trct  0.568  0.379  0.193                                          
## Afrcn.Am..T  0.157  0.147 -0.410  0.138                                   
## Nnctzns.n.T -0.002  0.106  0.044  0.068 -0.078                            
## High.BP     -0.003  0.243  0.067  0.114 -0.095  0.399                     
## Bing.Drnkng -0.284 -0.194 -0.298 -0.191  0.077  0.041  0.136              
## Cancer      -0.588 -0.198  0.180 -0.327 -0.073 -0.143 -0.381 -0.114       
## Asthma      -0.365 -0.209 -0.237 -0.183  0.091  0.096  0.169 -0.002  0.042
## Heart.Dises -0.148  0.075 -0.288 -0.151  0.249 -0.105 -0.015  0.059 -0.472
## COPD         0.564  0.041  0.136  0.279 -0.008  0.285  0.184  0.111 -0.270
## Smoking     -0.173  0.139 -0.169 -0.105 -0.059 -0.003 -0.070 -0.298  0.091
## Diabetes     0.072 -0.340 -0.107 -0.228 -0.308 -0.329 -0.526  0.044  0.242
## N.Physcl.Ac -0.176 -0.054  0.077 -0.035 -0.033 -0.219 -0.116  0.105  0.479
## Obesity      0.005  0.429  0.422  0.304  0.143  0.199 -0.084 -0.239  0.111
## Pr.Slpng.Hb -0.458 -0.404  0.142 -0.362 -0.360 -0.018 -0.190  0.092  0.142
## Pr.Mntl.Hlt -0.335  0.260 -0.060 -0.069  0.100 -0.179 -0.077  0.059  0.315
## Testing_Rat  0.168 -0.083 -0.026  0.012  0.043 -0.105 -0.014  0.006 -0.172
## Hsptlztn_Rt -0.131 -0.228 -0.120 -0.221 -0.039 -0.135 -0.139 -0.130  0.047
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.279                                                        
## COPD        -0.377 -0.561                                                 
## Smoking      0.078  0.205 -0.516                                          
## Diabetes    -0.119 -0.287 -0.111  0.238                                   
## N.Physcl.Ac  0.008 -0.384 -0.008 -0.329 -0.060                            
## Obesity     -0.274 -0.096  0.166 -0.199 -0.395 -0.063                     
## Pr.Slpng.Hb  0.069  0.251 -0.205  0.006 -0.011 -0.115 -0.168              
## Pr.Mntl.Hlt -0.237  0.086 -0.446  0.086  0.026  0.055  0.095 -0.187       
## Testing_Rat -0.351 -0.034  0.184  0.151  0.116 -0.302  0.099 -0.112 -0.098
## Hsptlztn_Rt  0.047  0.087 -0.111  0.098  0.099 -0.048 -0.043  0.005 -0.054
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.258
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling

Testing Rate

testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

Pink highlights the last 14 days.

day.first.case <- min(which(US.total$cases.total > 100))
n.days <- nrow(US.total)

twoweek.col <- c(rep("grey", n.days-day.first.case-13), rep("pink", 14))

par(mar = c(5,5,4,2))
barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$rise.cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Cases of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$rise.deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Deaths of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

COVID-19 Analysis

Katie Forthman

Tue May 12 09:22:01 2020

Click here for interactive plots:

Click here to see how cities and counties overlap:

Compare COVID stats to Neighborhood Factors

Compare COVID stats to 500 cities data and Neighborhood Factors

—-Linear Mixed Effects Model —-

Testing Rate